Particle Efficient Importance Sampling

Efficient High-Dimensional Importance Sampling

The paper describes a simple, generic and yet highly accurate Efficient Importance Sampling (EIS) Monte Carlo (MC) procedure for the evaluation of high-dimensional numerical integrals. EIS is based upon a sequence of auxiliary weighted regressions which actually are linear under appropriate conditions. It can be used to evaluate likelihood functions and byproducts thereof, such as ML estimators...

Efficient Post-processing with Importance Sampling

Introduction Texture filtering is a critical part in many rendering and post-processing methods. If we do it naively, the fragment shader needs to access the texture memory many times to fetch values in the neighborhood of the processed texel. This article presents an efficient filtering algorithm that minimizes the number of texture fetches. The algorithm is based on importance sampling and al...

Improving MCMC, using efficient importance sampling

This paper develops a systematic Markov Chain Monte Carlo (MCMC) framework based upon E cient Importance Sampling (EIS) which can be used for the analysis of a wide range of econometric models involving integrals without an analytical solution. EIS is a simple, generic and yet accurate Monte-Carlo integration procedure based on sampling densities which are chosen to be global approximations to ...

Efficient Importance Sampling for Binary Contingency Tables

Importance sampling has been reported to produce algorithms with excellent empirical performance in counting problems. However, the theoretical support for its efficiency in these applications has been very limited. In this paper, we propose a methodology that can be used to design efficient importance sampling algorithms for counting and test their efficiency rigorously. We apply our technique...

Efficient Nonparametric Importance Sampling for Bayesian Inference